The fact that we are living in a data-driven world is not lost on healthcare executives, administrators, and recruiters. Everyone involved in managing the day-to-day affairs of the healthcare and pharmaceutical industries relies heavily on data to do their jobs. It goes without saying that the value of one’s datasets rests in the quality of the data contained within.

We firmly believe in the power of high-quality data. We embrace the idea that what goes into data analysis determines what comes out the other end. Quality data going in leads to valuable analysis going out. Junk data going in only produces junk analyses.

So, what constitutes quality data? The following three principles:

1. Data Sources

Data source is paramount to quality. This truth is easily illustrated in a parlor game most of us are familiar with. That game involves gathering participants in a room before choosing one person to whisper a sentence to another. That second person then turns and whispers to a third person, and so on down the line. What comes out the other end is rarely identical to the first sentence uttered.

This is the way data works. Every time it is passed from one channel to the next, its integrity is potentially compromised. Therefore, we believe there is no substitute for first party data. When data is received directly from the party responsible for creating it, it is in its purest possible form. For the record, iMedical Data offers datasets consisting only of first-party data voluntarily provided by users of multiple websites our parent company owns and operates.

2. Data Vetting

First-party data is the starting point, but even it can prove to be inaccurate over time. For example, any data in our nurse’s database could be inaccurate simply because it was entered years ago. How do we address this? By properly vetting our data by way of healthcare systems, pharmaceutical companies, and other channels.

Data vetting is designed to compare old and new data in order to determine what constitutes genuinely accurate information. Old data that has not been put through a comprehensive vetting process is unreliable. You never know just how old or inaccurate it might be.

3. Data Relevance

Last but not least is data relevance. To understand the importance of this factor, consider the entire premise of big data. As a scientific endeavor, big data is all about harvesting as much data as possible and then finding ways to utilize it. In any big data scenario, the amount of usable data in a dataset relies heavily on the primary purpose for that data.

As a healthcare recruiter, you might be intensely interested in a physician’s database. Handing over a dataset that includes information about nurses and therapists would only make your job more difficult. You are only interested in data relevant to your task: recruiting physicians.

Relevance is an aspect often overlooked by those tasked with analyzing data. It is too easy to crunch numbers that are irrelevant to the task at hand. But doing so introduces inefficiency. It also adds noise that can distract from the goal.

Data is extremely valuable in the modern world. Your organization relies on it. So does ours. But the value in any dataset is commensurate with the quality of the data within. If you want to make the most of your data analysis, start with high quality data provided by an organization that knows what quality is. iMedical Data is that organization. We invite you to put our datasets to the test.

by Chris Lee (President IMD, LLC)